Flexible system and method for combining erasure-coded protection sets

ABSTRACT

Resource-efficient data protection is performed by generating meta chunks in storage systems that utilize erasure coding. During erasure coding with a k+m configuration, a data chunk can be divided into k data fragments, having indices 1 to k, that can be encoded by combining them with corresponding coefficients of a coding matrix, to generate coding fragments. Source portions that have a reduced set (e.g., less than k data fragments) of data fragments can be modified such that they are made complementary (e.g., that do not have common indices) without complete data re-protection. The complementary portions can then be combined to generate a meta chunk. The coding fragments of the complementary portions can be added to generate coding fragments for the meta chunk, which can then be utilized to recover data fragments of any of the source portions.

TECHNICAL FIELD

The subject disclosure relates generally to storage systems. Morespecifically, this disclosure relates to a flexible approach forcombining erasure-coded protection sets.

BACKGROUND

The large increase in amount of data generated by digital systems hascreated a new set of challenges for data storage environments.Traditional storage area network (SAN) and/or network-attached storage(NAS) architectures have not been designed to support data storageand/or protection at large multi-petabyte capacity levels. Objectstorage technology can be utilized to meet these requirements. Byutilizing object storage technology, organizations can not only keep upwith rising capacity levels but can also store these new capacity levelsat a manageable cost point.

Typically, a scale-out, cluster-based, shared-nothing object storagethat employs a microservices architecture pattern, for example, an ECS™(formerly known as Elastic Cloud Storage) can be utilized as a storageenvironment for a new generation of workloads. ECS™ utilizes the latesttrends in software architecture and development to achieve increasedavailability, capacity use efficiency, and performance. ECS™ uses aspecific method for disk capacity management, wherein disk space ispartitioned into a set of blocks of fixed size called chunks. User datais stored in these chunks and the chunks are shared. One chunk cancomprise fragments of several user objects. Chunk content is modified inan append mode. When chunks become full, they are sealed, and thecontent of sealed chunks is immutable. Oftentimes, chunks can comprise areduced set of data fragments. This increases capacity overheads on dataprotection and there are some cases when the overheads may beunreasonably high.

The above-described background relating to ECS™ is merely intended toprovide a contextual overview of some current issues and is not intendedto be exhaustive. Other contextual information may become furtherapparent upon review of the following detailed description.

SUMMARY

The following presents a simplified summary of the specification inorder to provide a basic understanding of some aspects of thespecification. This summary is not an extensive overview of thespecification. It is intended to neither identify key or criticalelements of the specification nor delineate the scope of any particularembodiments of the specification, or any scope of the claims. Its solepurpose is to present some concepts of the specification in a simplifiedform as a prelude to the more detailed description that is presented inthis disclosure.

Example systems and methods, and other embodiments, disclosed hereinrelate to facilitating capacity management in distributed storagesystems. In one example embodiment, a system is disclosed that comprisesa processor and a memory that stores executable instructions that, whenexecuted by the processor, facilitate performance of operations.Moreover, the operations comprise selecting source chunks of data storedwithin a storage system that are determined to have fewer than a definednumber of data fragments, wherein a first source chunk, of the sourcechunks, is divided into first indexed data fragments, and wherein thefirst indexed data fragments are erasure-coded to generate first codingfragments associated with the first source chunk. Further, theoperations comprise modifying an index of a data fragment of the firstindexed data fragments and modifying the first coding fragments of thefirst source chunk to convert the source chunks into complementarysource chunks; wherein the complementary source chunks do not have datafragments with a common index, and wherein the modifying the firstcoding fragments generates modified coding fragments independent ofperforming an erasure-coding operation. Furthermore, the operationscomprise generating a meta chunk based on combining the complementarysource chunks subsequent to the modifying, wherein combined codingfragments, associated with the meta chunk, are determined based onadding the modified coding fragments with second coding fragmentsgenerated based on erasure coding second indexed data fragments of asecond source chunk of the source chunks

Another example embodiment of the specification relates to a method thatcomprises determining, by a system comprising a processor, source chunksof data stored within a storage system that have fewer than a definednumber of data fragments, wherein a first source chunk, of the sourcechunks, is divided into first indexed data fragments, and wherein thefirst indexed data fragments are erasure-coded to generate first codingfragments associated with the first source chunk. The method furthercomprises adjusting an index of a data fragment of the first indexeddata fragments and updating the first coding fragments of the firstsource chunk to convert the source chunks into complementary sourcechunks; wherein the complementary source chunks do not have datafragments with a common index, and wherein the modifying the firstcoding fragments generates updated coding fragments independent ofperforming an erasure-coding operation. Subsequent to the updating, themethod comprises combining the complementary source chunks to generate ameta chunk, wherein combined coding fragments, associated with the metachunk, are determined based on adding the updated coding fragments withsecond coding fragments generated based on erasure coding second indexeddata fragments of a second source chunk of the source chunks.

Another example embodiment of the specification relates to acomputer-readable storage medium comprising instructions that, inresponse to execution, cause a computing node device comprising aprocessor to perform operations, comprising encoding chunks of datastored in an object storage system, wherein the chunks comprise datafragments that have been assigned respective indices, and wherein theencoding comprises combining, based on the respective indices, the datafragments with corresponding encoding coefficients to generaterespective. Further, the operations comprise modifying at least onechunk of a group of the chunks to ensure that the group of the chunksdoes not have data fragments having common indices, wherein the group ofthe chunks are determined not to have more than a defined number of datafragments, and wherein the modifying results in a generation of updatedcoding fragments, that correspond to the group of the chunks,independent of performing an erasure coding operation to re-encode theat least one chunk. Furthermore, subsequent to the modifying, theoperations comprise combining the group of the chunks to generate a metachunk; and based on a summation of the updated coding fragments,determining meta chunk coding fragments that are to be employed torecover at least a portion of the group of the chunks during a failurecondition.

The following description and the drawings set forth certainillustrative aspects of the specification. These aspects are indicative,however, of but a few of the various ways in which the principles of thespecification may be employed. Other advantages and novel features ofthe specification will become apparent from the detailed description ofthe specification when considered in conjunction with the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example cloud data storage system that facilitatescombining erasure-coded protection sets for meta chunk generation,according to one or more example implementations.

FIG. 2 illustrates an example layout of a chunk within an object storagesystem in accordance with an aspect of the specification.

FIG. 3 illustrates an example system for combining protection sets,according to an aspect of the subject disclosure.

FIG. 4 illustrates an example system for generating complementary dataportions.

FIG. 5 illustrates an example system that facilitates efficient dataprotection by employing meta chunks.

FIG. 6 illustrates an example system that facilitates efficient datarecovery by employing meta chunks.

FIGS. 7A-7D depict example embodiments that illustrate a reduction ofcapacity overheads on data protection without complete datare-protection.

FIG. 8 illustrates an example method for determining a combinedprotection set for partially complementary data portions in accordancewith an aspect of this disclosure.

FIG. 9 illustrates high-level architecture of an ECS™ cluster thatfacilitates combining erasure-coded protection sets.

FIG. 10 illustrates a block diagram of an example computer operable toexecute the disclosed distributed storage system architecture.

DETAILED DESCRIPTION

One or more embodiments are now described with reference to thedrawings, wherein like reference numerals are used to refer to likeelements throughout. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea thorough understanding of the various embodiments. It may be evident,however, that the various embodiments can be practiced without thesespecific details, e.g., without applying to any particular networkedenvironment or standard. In other instances, well-known structures anddevices are shown in block diagram form in order to facilitatedescribing the embodiments in additional detail.

The term “cloud” as used herein can refer to a cluster of nodes (e.g.,set of network servers), for example, within a distributed objectstorage system, that are communicatively and/or operatively coupled toeach other, and that host a set of applications utilized for servicinguser requests. In general, the cloud computing resources can communicatewith user devices via most any wired and/or wireless communicationnetwork to provide access to services that are based in the cloud andnot stored locally (e.g., on the user device). A typical cloud-computingenvironment can include multiple layers, aggregated together, thatinteract with each other to provide resources for end-users.

Example systems and methods disclosed herein, in one or moreembodiments, relate to cloud storage systems that utilize erasure codingfor data protection, such as, but not limited to an ECS™ platform. TheECS™ platform combines the cost advantages of commodity infrastructurewith the reliability, availability, and serviceability of traditionalarrays. In one aspect, the ECS™ platform can comprise a cluster of nodes(also referred to as “cluster” herein) that delivers scalable and simplepublic cloud services with the reliability and/or control of aprivate-cloud infrastructure. Moreover, the ECS™ platform comprises ascale-out, cluster-based, shared-nothing object storage, which employs amicroservices architecture pattern. The ECS™ platform can supportstorage, manipulation, and/or analysis of unstructured data on a massivescale on commodity hardware. As an example, ECS™ can support mobile,cloud, big data, content-sharing, and/or social networking applications.ECS™ can be deployed as a turnkey storage appliance or as a softwareproduct that can be installed on a set of qualified commodity serversand/or disks. The ECS™ scale-out and geo-distributed architecture is acloud platform that can provide at least the following features: (i)lower cost than public clouds; (ii) unmatched combination of storageefficiency and data access; (iii) anywhere read/write access with strongconsistency that simplifies application development; (iv) no singlepoint of failure to increase availability and performance; (v) universalaccessibility that eliminates storage silos and inefficient extract,transform, load (ETL)/data movement processes; etc.

In an aspect, ECS™ does not rely on a file system for disk capacitymanagement. Instead, ECS™ partitions disk space into a set of blocks offixed size called chunks (e.g., having a chunk size of 128 MB). All userdata is stored in these chunks and the chunks are shared. Typically, achunk can comprise fragments of several different user objects. Thechunk content can be modified in an append-only mode. When a chunkbecomes full, it can be sealed, and the content of a sealed chunk isimmutable. Further, ECS™ does not employ traditional data protectionschemes like mirroring or parity protection. Instead, ECS™ utilizeserasure coding for data protection. A chunk can be divided into indexedportions (e.g., data fragments), for example, by a chunk manager. Anindex of a data fragment can be a numerical value assigned by the chunkmanager and utilized for erasure coding. Moreover, the index of a datafragment can be utilized to determine a coefficient, within an erasurecoding matrix (e.g., the index can be utilized to determine a row and/orcolumn of the matrix), which is to be combined (e.g., multiplied) withthe data fragment to generate a corresponding coding fragment for thechunk. Although the systems and methods disclosed herein have beendescribed with respect to object storage systems (e.g., ECS™), it isnoted that the subject specification is not limited to object storagesystems and can be utilized for most any storage systems that utilizeerasure coding for data protection and chunks for disk capacitymanagement. Thus, any of the embodiments, aspects, concepts, structures,functionalities or examples described herein are non-limiting, and thetechnology may be used in various ways that provide benefits andadvantages in computing and data storage in general.

Oftentimes erasure-coded storage systems create a data protection unit(e.g., meta chunk), which combines two or more source chunks having areduced sets of data fragments, to increase capacity use efficiencywithout verification and/or data copying. However, generation of thisdata protection unit requires complete data re-protection. In otherwords, an encoding operation (e.g., erasure coding operation) must beperformed using all/combined data fragments of the meta chunk togenerate new coding fragments. This is a very resource-demandingoperation, especially for GEO erasure coding (the use-case is describedin detail in the following paragraphs). Resource-efficient dataprotection can be utilized by combining two or more complementary dataportions (e.g., that do not have data fragments with the same index) bysimply summing their protection sets (e.g., coding fragments) togenerate a combined protection set (e.g., a protection set with greaternumber of data fragments). However, finding degraded chucks that arecomplementary can be rare. Accordingly, systems and methods disclosedherein relate to modifying one or more data portions (e.g., altering oneor more data fragment indices and modifying their coding fragment(s))that have a reduced sets of data fragments, to generate sets of two ormore complementary data portions that can be efficiently combined into ameta chunk.

FIG. 1 shows part of a cloud data storage system such as ECS™ comprisinga zone (e.g., cluster) 102 of storage nodes 104(1)-104(M), in which eachnode is typically a server configured primarily to serve objects inresponse to client requests (e.g., received from clients 108). The nodes104(1)-104(M) can be coupled to each other via a suitable datacommunications link comprising interfaces and protocols such as, but notlimited to, Ethernet block 106.

Clients 108 can send data system-related requests to the cluster 102,which in general is configured as one large object namespace; there maybe on the order of billions of objects maintained in a cluster, forexample. To this end, a node such as the node 104(2) generally comprisesports 112 by which clients connect to the cloud storage system. Exampleports are provided for requests via various protocols, including but notlimited to SMB (server message block), FTP (file transfer protocol),HTTP/HTTPS (hypertext transfer protocol), and NFS (Network File System);further, SSH (secure shell) allows administration-related requests, forexample.

Each node, such as the node 104(2), includes an instance of an objectstorage system 114 and data services. For a cluster that comprises a“GEO” zone of a geographically distributed storage system, at least onenode, such as the node 104(2), includes or coupled to reference trackingasynchronous replication logic 116 that synchronizes the cluster/zone102 with each other remote GEO zone 118. Note that ECS™ implementsasynchronous low-level replication, that is, not object levelreplication. Typically, organizations protect against outages orinformation loss by backing-up (e.g., replicating) their dataperiodically. During backup, one or more duplicate or deduplicatedcopies of the primary data are created and written to a new disk or to atape, for example within a different zone. The term “zone” as usedherein can refer to one or more clusters that is/are independentlyoperated and/or managed. Different zones can be deployed within the samelocation (e.g., within the same data center) and/or at differentgeographical locations (e.g., within different data centers).

In general, and in one or more implementations, e.g., ECS™, disk spaceis partitioned into a set of large blocks of fixed size called chunks;user data is stored in chunks. Chunks are shared, that is, one chunk maycontain segments of multiple user objects; e.g., one chunk may containmixed segments of some number of (e.g., three) user objects.

A chunk manager 120 can be utilized to manage the chunks and theirprotection (e.g., via erasure coding (EC)). Erasure coding was createdas a forward error correction method for binary erasure channel.However, erasure coding can be used for data protection on datastorages. During erasure coding (e.g., utilizing a k+m configuration),the chunk manager 120 can partition a piece of data (e.g., chunk) into kdata fragments of equal size. During encoding, redundant m codingfragments are created so that the system can tolerate the loss of any mfragments. Typically, the chunk manager 120 can assign indices to thedata fragments (and corresponding coding fragments). In an example, anindex can be a numerical value (e.g., 1 to k) that is utilized forerasure coding. Moreover, the index of a data fragment can be utilizedto determine a coefficient, within an erasure coding matrix, which is tobe combined (e.g., multiplied) with the data fragment to generate acorresponding coding fragment for the chunk. For example, an index valuecan specify a row and/or column of the coefficient within the erasurecoding matrix. As an example, the indices can be assigned based on adefined sequence, in a random order, based on a defined criterion (e.g.,to increase probability of complementary data fragments), based onoperator preferences, etc. The process of coding fragments creation iscalled encoding. The process of data fragments recovery using availabledata and coding fragments is called decoding.

In one example embodiment, GEO erasure coding can also be utilized,wherein if a distributed storage 100 is to tolerate the loss of any mzones/clusters/chunks, then GEO erasure coding can begin at each zone byreplicating each new chunk to at least m remote zones. As a result,there are m backup copies of each chunk. Typically, there is one primarybackup copy, which can be utilized for encoding. Encoding is performedby one zone for primary backup chunks and other zones replicate to it.Once a zone has k primary chunks replicated from different remote zones,the zone can perform encoding using the chunks replicated to it as datafragments. The chunk size is fixed, in ECS™, with padding or other datato complement, wherein the other data is added as needed. The result ofencoding is m data portions of a chunk size. They are stored as chunksof a specific type called coding chunks. After encoding is complete, thezone can store one coding chunk locally and move other m−1 coding chunksto remote zones making sure all the k+m data and coding chunks arestored at different zones whenever possible. Afterwards, the primarybackup chunks used for encoding and their peer backup chunks at otherzones can be deleted.

According to an aspect, the chunk manager 120 can efficiently generatecombined data protection sets during consolidating two or moreerasure-coded data portions (e.g., normal/source chunks) that have areduced sets of data fragments. As an example, chunk manager 120 canverify that the two or more erasure-coded data portions arecomplementary (e.g., do not have data fragments with the same index) andperform a summing operation to combine their corresponding codingfragments to generate a combined protection set. In one aspect, chunkmanager 120 can increase a number of complementary data portions basedon altering one or more indices of data fragments and correspondinglymodifying coding fragment(s) associated with the data fragment(s) whoseindex has been altered.

A CPU 122 and RAM 124 are shown for completeness; note that the RAM 124can comprise at least some non-volatile RAM. The node includes storagedevices such as disks 126, comprising hard disk drives and/orsolid-state drives. It is noted that the storage devices can comprisevolatile memory(s) or nonvolatile memory(s), or both volatile andnonvolatile memory(s). Examples of suitable types of volatile andnon-volatile memory are described below with reference to FIG. 10. Thememory (e.g., data stores, databases, tables, etc.) of the subjectsystems and methods is intended to comprise, without being limited to,these and any other suitable types of memory.

FIG. 2 illustrates an example layout 200 of a chunk within an objectstorage system in accordance with an aspect of the specification. In anaspect, disk space of the object storage system can be partitioned intoa set of blocks of fixed size called chunks. As an example, the chunksize can be 128 MB. Typically, user data is stored in these chunks andthe chunks are shared. As shown in FIG. 2, a chunk 202 can comprisesegments of several user objects (e.g., object 1 segments 204, object 2segments 206, and object 3 segments 208). It is noted that the chunklayout depicted in FIG. 2. is one example and the chunks can have mostany other layout with segments from one or more user objects. Chunkcontent is modified in an append-only mode. When the chunk becomes fullenough, it is sealed. After the chunk is sealed, its content isimmutable.

In an aspect, the chunk can be protected by employing matrix-basederasure coding. During the erasure coding, a chunk can be divided into kdata fragments of equal size. To encode the chunk, redundant m codingfragments are created so that the system can tolerate the loss of any mfragments. The process of generating the coding fragments is calledencoding. The process of data fragments recovery using available dataand coding fragments is called decoding. As an example, the encodingoperation can be represented with the equation below:

C _(i)=Σ_(j=1) ^(k) C _(i,j)   (1)

wherein,

C _(i,j) =X _(i,j) *D _(j)   (2)

and wherein, X_(i,j) is a defined coefficient from a coding matrix(e.g., wherein i, j, and/or k can be most any integer). Further, j is anindex assigned to the data fragment. It is noted that D_(j) areindependent data fragments and C_(i) are coding fragments.

Additionally, or optionally, the systems and methods disclosed hereincan support geographically distributed setups (GEO) comprising two ormore zones. GEO can be used to provide an additional protection of userdata by means of replication. Replication works at the chunk level,wherein a backup copy of a chunk stored in a primary zone can bereplicated to one or more secondary zones. Each zone protects the chunksit stores. If a copy of a chunk becomes unavailable, it can be recoveredusing its other copy. This process is called GEO recovery. In case ofGEO erasure coding, remote backup copies of data chunks are used as datafragments and coding fragments created for such data fragments arestored as coding chunks. It is noted that the aspects disclosed hereinwith respect to generating complementary pairs of data portions andcombining them to produce a meta chunk can be applied to GEO erasurecoding.

Referring now to FIG. 3, there illustrated is an example system 300 forcombining protection sets, according to an aspect of the subjectdisclosure. In one aspect, the chunk manager 120 can efficiently protectchunks, for example, by employing erasure coding (or GEO/distributederasure coding). As an example, the chunk manager 120 can includefunctionality as more fully described herein, for example, as describedabove with regard to system 100. It is noted that the term “datafragment” as used herein can comprise either a traditional data fragmentof a chunk (e.g., erasure coding use case) or a data chunk within a zone(e.g., GEO/distributed erasure coding use case); and the term “codingfragment” as used herein can comprise either a traditional codingfragment of a chunk (e.g., erasure coding use case) or a coding chunkwithin a zone (e.g., GEO/distributed erasure coding use case).

In one aspect, a source portion detection component 302 can be utilizedto determine two or more source portions (e.g., comprising datafragments and corresponding coding fragments). As an example, a sourceportion comprises fewer data fragments than a maximum number (k) of datafragments that can be stored within a chunk. There can be several caseswhen a portion has fewer than k data fragments. In one example, a datachunk can be sealed before it gets filled. In this example scenario, astorage system stores only one or more (l) first data fragments, thedata fragments with user data and the remaining k−1 data fragmentscontain no user data, so they are not stored. This scenario is normallya result of a failure or a node restart. As an example, when a storagesystem survives a period of instability, the system may producethousands of poorly filled chunks with just one or two data fragments.In another example, a quasi-compacting garbage collection processdetects unused blocks within data chunks, reclaims their capacity, andre-uses the freed capacity to create new composite chunks. With thequasi-compacting garbage collection on, chunks degrade gradually. Thatis, a chunk can “lose” its data fragments at its beginning, its end, orin the middle. The number of lost fragments grows with the lapse oftime. In yet another example, deletion of data chunks can lead to asituation wherein a protection set created with GEO erasure coding cancomprise fewer than k data chunks. Coding chunks from such a protectionset are partial coding chunks.

Typically, a data store 304 (e.g., chunk table) can store informationabout portions/chunks, for example, the number of data fragments storedin each portion/chunk and their indices. The source portion detectioncomponent 302 can utilize this information to identify two or moresource portions that can be unified to reduce system capacity overheads.As an example, source portion detection component 302 can determinesource portions that when combined have k (or fewer than k) datafragments, periodically, on-demand, in response to detecting an event,at a specified time, etc. Initially, the source portion detectioncomponent 302 can select source portions that determined to becomplementary. Two or more data portions are said to be complementarywhen there is no pair of data portions that have data fragments with thesame index. In other words, for each data fragment index there is one orzero data fragments among all complementary data portions. As anexample, indices are assigned to data fragments to facilitate the ECencoding operation, for example, by the chunk manager. Moreover, acoding fragment is generated based on combining (e.g., multiplying) adata fragment with a coding matrix coefficient that is selected based onthe index of the data fragment.

Further, in one embodiment, the source portion detection component 302can determine source portions that are not fully complementary but canbe made complementary without high overheads. Moreover, two or more dataportions are not fully complementary (also referred to herein aspartially complementary) when there is at least one data fragment indexfor which there is just one data fragment among the data portions andthere is at least one data fragment index for which there are two ormore data fragments among the data portions. A complementary portiongeneration component 308 can be utilized to convert partiallycomplementary data portions into complementary data portions beforeuniting them into a meta chunk. In one aspect, the complementary portiongeneration component 308 can modify an index of one or more datafragments of one or more data portions to create a complementary set ofdata portions. It is noted that the indices remain the same from thesource data chunk's point of view and that the alteration of indices isonly for producing a meta chunk.

When one or more indices are altered, coding fragments corresponding tothe altered indices are also altered by employing the complementaryportion generation component 308 without performing complete datare-protection. For matrix-based erasure coding, coding fragments areproduced using the equations (1) and (2). When index of one or more datafragments is altered, the complementary portion generation component 308can replace coding fragments Ci with updated C′i without completere-encoding of a data portion, for example, based on the followingformula:

C′ _(i) =C _(i −X) _(ij) *D _(j) +X _(j,g) *D _(j) =C _(i)−(X _(i,j) −X_(i,g))*D _(j)   (3)

wherein, j and g are original and new indices of a data fragmentrespectively. A new coding fragment is derived from an original codingfragment via withdrawing content of a data fragment multiplied by anoriginal coefficient from a coding matrix and adding content of the samedata fragment multiplied by a new coefficient from the same codingmatrix. As the second part of the formula (3) shows, withdrawing andadding can be done in one step since coefficients from a coding matrixare natural numbers.

According to an aspect, a combination component 306 can create acombined protection set (e.g., comprising a meta chunk) by combining thecomplementary portions (e.g., selected by the source portion detectioncomponent 302 and/or generated by the complementary portion generationcomponent 308). It is noted that physical capacity is not allocated forthe meta chunk. However, the combination component 306 can create alayout within the newly created data portion (e.g., meta chunk). Thislayout can map the data fragments of the source portions to the datafragments of the newly created data portion. This mapping can be storedwithin the data store 304. The creation of the new data portion does notimpact data access because data location can still be specified usingsource portions, which remain the same. This assures an advantage overconventional copying garbage collection. Further, the generation of thenew data portion does not require a resource-demanding verificationprocedure to be performed. Further, utilization of the new data portiondoes not require user data location updates.

In one embodiment, the combination component 306 can unite thecomplementary data portions via a simple summing operation as follows:There are n protection sets for n complementary data portions (e.g., nis an integer greater than 1). Each p-th protection set can be describedwith an incomplete set of data fragments {D_(j) ^(p)} and a complete setof coding fragments {C_(i) ^(p)}. In this example scenario, the union ofthe protection sets (U) would comprise: (i) a union of n sets of datafragments {D_(j) ^(p)}, wherein each data fragment preserves its initialindex. The result of this union can be indicated as {D_(j) ^(U)}; and(ii) a set of m coding fragments {C_(i) ^(U) 56 , wherein

C_(i) ^(U)=Σ_(p=1) ^(n)C_(i) ^(p)   (4)

Performing data protection at the level of the combined data portion(e.g., meta chunk) allows reduction of capacity overheads on dataprotection by n times, where n is a number of normal/source dataportions united. Moreover, n*m coding fragments for source data portionsare replaced with just m coding fragments of the standard size for aunited data portion. Accordingly, system 300 can reduce capacityoverheads without complete data re-protection, resulting in a processthat is less resource demanding. This is especially advantageous in caseof GEO erasure coding, wherein complete data re-protection after metachunk generation is substantially resource demanding.

FIG. 4 illustrates is an example system 400 for generating complementarydata portions, according to an aspect of the subject disclosure. It isnoted that the chunk manager 120, source portion detection component302, data store 304, combination component 306, and complementaryportion generation component 308 can include functionality as more fullydescribed herein, for example, as described above with regard to systems100 and 300.

According to an embodiment, the complementary portion generationcomponent 308 can utilize information, such as, but not limited to, oneor more operator-defined policies 402 to optimize the generation of thecomplementary data portions. In one example, the one or more policies402 can specify when to initiate generation of the complementary dataportions (e.g., periodically, in response to an event, during an idletime period, after combining all available complementary portions intometa chunks, etc.). In another example, the one or more policies 402 canspecify which source data portions should be converted and/or a priorityassociated with selection and/or conversion of source data portions(e.g., convert data portions that require changing only one index of adata fragment prior to converting data portions that require changing oftwo indices of data fragments to generate complementary sets of dataportions).

In one aspect, the data store 304 can store a list (and/or other datastructure) of degraded chunks (e.g., a chunk that has fewer than k datafragments), for example, sorted in an ascending or descending order ofdata fragments. The complementary portion generation component 308 canutilize the stored data to prioritize conversion and/or combination ofthe data portions. For example, chunks having the fewer data fragmentscan be converter and/or combined before chunks that have more datafragments.

FIG. 5 illustrates an example system 500 that facilitates efficient dataprotection by employing meta chunks. In one aspect, the chunk manager120 and the combination component 306 can include functionality as morefully described herein, for example, as described above with regard tosystems 100, 300, and 400. Typically, erasure coding is utilized fordata protection. Moreover, when data is protected with erasure coding,the overheads on data protection are be calculated as m/k. In asituation when a chunk has fewer data fragments (l) the overheads arem/l. Thus, the fewer l, the greater capacity overheads on dataprotection and there are cases when the overheads may be unreasonablyhigh. Conventional copying garbage collection mechanisms are too slow tomake a difference. In contrast, system 500 can efficiently protectcomplementary data portions that have reduced sets of data fragments(e.g., by employing meta chunks) without complete data re-protection. Inone aspect groups of data portions that are partially complementary(e.g., not fully complementary) can be combined to generatecomplementary data portions (e.g., by the complementary portiongeneration component 308).

As described in detail supra, the combination component 306 can generatea new meta chunk. In an example, a layout can be created within the newmeta chunk that maps the data fragments of the source portions to thedata fragments of the new meta chunk. Further, the combination component306 can combine (e.g., add) the coding fragments of the complementarydata portions to generate and store m coding fragments for the new metachunk. In an aspect, metadata of the source portions (e.g., stored indata store 304) can be updated to reference their meta chunk.

Furthermore, a cleanup component 502 can be utilized to delete codingfragments associated with the source portions (e.g., that werepreviously generated to protect individual source portions). Withreference to equation (4), after the set {C_(i) ^(U)} is generated andsaved, the combination component 306 can delete the source sets ofcoding fragments {C_(i) ^(p)}. As an example, for n source portions, thecleanup component 502 can delete n sets of m coding fragments, one setper source portion from the initial set (e.g., including the updatedcoding fragments). Source meta chunks (if any) can also be deleted bythe cleanup component 502. Performing data protection at the meta chunklevel (instead of source chunk level) allows to reduce the capacityoverheads by n times, where n is a number of source portions united inone meta chunk. Moreover, n*m previously generated coding fragments forthe source portions are replaced with just m coding fragments of thestandard size for a meta chunk.

FIG. 6 illustrates an example system 600 that facilitates efficient datarecovery by employing meta chunks. In one aspect, a recovery component602 can be utilized to recover one or more source portions that havebeen protected at a meta chunk level. It is noted that the data store304 can include functionality as more fully described herein, forexample, as described above with regards to systems 300-400.

In one aspect, a failure detection component 604 can determine that afailure condition has occurred. For example, a failure condition cancomprise a loss and/or unavailability of data (e.g., one or more dataand/or coding fragments) due to data corruption, hardware failures, datacenter disasters, natural disasters, malicious attacks, etc. Moreover,the failure detection component 604 can detect the unavailability and/orloss at the source portion level. A decoding component 606 can performrecovery of the data fragment at the meta chunk level. For example, thedecoding component 606 can employ a decoding matrix that corresponds tothe coding matrix utilized during erasure coding. Further, the decodingcomponent 606 can utilize mapping information (e.g., that maps sourceportions to a meta chunk) that is, for example, stored within the datastore 304, to determine the meta chunk that is to be recovered. Thedecoding results in a recovery of the data fragments, which can then bestored as a part of its parent source portion (e.g., by employing thedata storage component 608).

FIGS. 7A-7D depict example embodiments (700, 725, 750, and 775,respectively) that illustrate reduction of capacity overheads on dataprotection without complete data re-protection. FIG. 7A illustrates twoexample data portions, portion A 702 and portion B 704, that have areduced set of data fragments, 7061-7062 respectively, and are not fullycomplementary. Moreover, in this example scenario, a 4+2 (k=4, m=2)erasure coding protection configuration/protocol is applied for dataprotection and coding fragments 708 ₁-708 ₂ are generated for eachportion A-B. Although only two portions are depicted, it is noted thatthe subject disclosure is not limited to three portions with a 4+2protection configuration, and most any number (greater than 1) ofportions with most any erasure coding protection scheme can be utilized.

In this example, portion A 702 comprises two data fragments, D₂ ^(A) andD₄ ^(A) (e.g., data fragments D₁ ^(A) and D₃ ^(A), can be deleted by aquasi-compacting garbage collector), and portion B 704 comprises twodata fragments D₁ ^(B) and D₄ ^(B) (e.g., the portion was sealedprematurely). Altogether the portions above comprise 4 (k) datafragments and 6 (3*m) coding fragments. The overheads on data protectionis 3/2 (6/4) instead of target 1/2 (2/4).

In one aspect, the source portion detection component 302 can determinethat the portions A and B are degraded (e.g., have fewer than k datafragments) but are not fully complementary. Moreover, the portions A andB have at least one data fragment index (e.g., index 1 and index 2) forwhich there is just one data fragment (e.g., D₂ ^(A) and D₁ ^(B)) amongthe data portions and at least one data fragment index (e.g., index 4)for which there are two or more data fragments (e.g., D₄ ^(A) and D₄^(B)) among the data portions. According to an embodiment, thecomplementary portion generation component 308 can convert the portionsA and/or B into a complementary set of portions to enable a resourceefficient mechanism to combine the portions into a meta chunk andaccordingly reduce overheads on data protection.

FIG. 7B depicts an embodiment 725, wherein data portion B 704 has beenmodified to make portions A and B complementary (e.g., for each datafragment index (D_(i)) there is only one or zero data fragments amongall complementary data portions). For example, the complementary portiongeneration component 308 can change the index of D₄ ^(B) from 4 to 3 tomake the portions A and B complementary. It is noted that all indicesremain the same from the source data portion point of view. Alterationof indices is for generating accurate coding chunks to producing themeta chunk only. In addition to changing the index, the coding fragmentscan be updated from C₁ ^(B) and C₂ ^(B) (7082) to C′₁ ^(B) and C′₂ ^(B)(708′₂) based on utilizing equation (3) without complete re-encoding ofthe data fragments 706′₂.

FIG. 7C depicts an embodiment 750, wherein the portions A and B thathave been made complementary, can be combined (e.g., via the combinationcomponent 306) into a meta chunk, portion U 710, having data fragments706 _(3.) It is noted that the combination does not require transferand/or processing of the data fragments 706 ₁-706 ₂. According to anembodiment, the combination component 306 can add the coding fragments708 ₁-708 ₂ to generate coding fragments 7083 for the portion U 710. Forexample, C₁ ^(U)=C₁ ^(A)+C′₁ ^(B) and C₂ ^(U)=C₂ ^(A)+C′₂ ^(B).

FIG. 7D illustrates an example final layout of data and codingfragments. Coding fragments 7083 can be utilized to protect datafragments within portion U 710 that belong to the two source portions(portion A 702 and portion B 704). The source portions (portion A 702and portion B 704) can be linked with the portion U 710 (e.g., viametadata stored in the data store 304) and the individual codingfragments (708 ₁, 708 ₂, and 708′₂) created for the source portions canbe deleted (e.g., via the cleanup component 502).

Since encoding is performed at meta chunk level, there are four datafragments (k) 706 ₃ and two (m) coding fragments 708 ₃, the target levelof overheads on data protection 1/2 (m/k) can be achieved. Dataprotection with meta chunks is a lightweight alternative to the copyinggarbage collector in ECS™. It can increase capacity use efficiencywithout verification and/or data copying. Although FIGS. 7A-7D depictthe generation and encoding of a meta chunk (e.g., portion U 710)subsequent to encoding of individual source portions (portion A 702 andportion B 704), it is noted that the subject disclosure is not solimited and that the source portions can be identified and employed togenerate a meta chunk, before they have been individually encoded.

FIG. 8 illustrates flow diagrams and/or methods in accordance with thedisclosed subject matter. For simplicity of explanation, the flowdiagrams and/or methods are depicted and described as a series of acts.It is to be understood and appreciated that the various embodiments arenot limited by the acts illustrated and/or by the order of acts, forexample acts can occur in various orders and/or concurrently, and withother acts not presented and described herein. Furthermore, not allillustrated acts may be required to implement the flow diagrams and/ormethods in accordance with the disclosed subject matter. In addition,those skilled in the art will understand and appreciate that the methodscould alternatively be represented as a series of interrelated statesvia a state diagram or events. Additionally, it should be furtherappreciated that the methods disclosed hereinafter and throughout thisspecification are capable of being stored on an article of manufactureto facilitate transporting and transferring such methods to computers.The term article of manufacture, as used herein, is intended toencompass a computer program accessible from any computer-readabledevice or computer-readable storage/communications media.

Referring now to FIG. 8, there illustrated is an example method 800 fordetermining a combined protection set for partially complementary dataportions. In an aspect, method 800 can be performed within an objectstorage system, for example, ECS™. The object storage system can employchunks for disk capacity management, wherein the disk space ispartitioned into a set of blocks of fixed/defined size (e.g., 128 MB)called chunks. All user data can be stored in the chunks and the chunkscan be shared between different users. For example, a chunk can comprisefragments of several dozens of user objects. However, one chunk can alsocomprise fragments of thousands of user objects (e.g., in case of emailarchives). Chunk content can be modified in an append-only mode. When achunk becomes full enough, it can be sealed and once sealed, the contentof the chunk is immutable. Oftentimes, chunks can be sealed before theyare full and/or can have fewer than the maximum/defined number of datafragments (e.g., defined k data fragments for a k+1 erasure codingprotection protocol). Accordingly, at 802, source portions with areduced set of data fragments that are not fully complementary can bedetermined. For example, complementary portions are those portions thatdo not have data fragments having the same indices, while not fullycomplementary portions are those portions that have least one datafragment index for which there is just one data fragment among the dataportions and at least one data fragment index for which there are two ormore data fragments among the data portions. In an aspect, the sourceportions can be selected, for example, based on information storedwithin a chunk table (or other system data store). A source portion cancomprise a reduced set of data fragments and corresponding codingfragments that have been generated based on erasure coding of the datafragments. In case of distributed erasure coding, the source portion cancomprise a reduced set of data chunks and corresponding coding chunksthat have been generated based on GEO erasure coding of the data chunks.Although the systems and methods herein are described with respect todata fragments that have been erasure coded, it is noted that thesubject embodiments can also be applied to data chunks that are GEOerasure coded.

At 804, one or more of the source portions can be modified to make thesource portions complementary. For example, an index of a data fragmentcan be changed, and corresponding coding fragments can be updated (e.g.,by utilizing equation (3) without re-encoding the data fragments).Further, at 806, the data fragments of the source portions can becombined as a meta chunk. In one aspect, physical capacity is notallocated for the meta chunk, but a layout can be created within the newmeta chunk. This layout can link the data fragments of the sourceportions involved to the data fragments of the meta chunk. At 808, thesource portions can be linked to the meta chunk. As an example, themetadata (e.g., stored in a chunk table) of the source portions can beupdated to include a reference to the meta chunk.

At 810, the coding fragments of the source portions can be added togenerate coding fragments for the meta chunk. This set of codingfragments can be utilized to recover data fragments of one or more ofthe source portions (e.g., subsequent to a failure condition). Further,at 812, the individual sets of coding fragments, that were previouslygenerated by individually encoding each source portion, can be deleted.In one aspect, if the source portions comprise one or more previouslygenerated meta chunks, the previously generated meta chunks can also bedeleted. Further, in this example scenario, the source portions of theone or more previously generated meta chunks can be linked to the newmeta chunk.

Typically, when a failure condition occurs within an object storagesystem (e.g., ECS™), wherein one or more the data fragments of thesource portions have become corrupted, unavailable, and/or lost, it canbe identified that the unavailable data fragment(s) belong to a sourceportion that is associated with the meta chunk. For example, themetadata associated with the source portion can provide a referenceand/or link to the meta chunk that is to be recovered. Moreover, thedata fragment(s) can be recovered at a meta chunk level. For example, adecoding operation can be performed by employing the coding fragments ofthe meta chunk. Further, the recovered data fragment(s) can be stored asa part of the source portion.

The systems and methods (e.g., 100-800) disclosed herein provide atleast the following non-limiting advantages: (i) reduced capacityoverheads during data protection without complete data re-protection;and (ii) creation of meta chunks does not impact data access becausedata location is still specified using normal chunks, which remain thesame. Use of meta chunks does not require neither resource-demandingverification procedure nor user data location updates; (iii) summationof the coding fragments of complementary source portions allows asimple, efficient, and relatively quicker technique for generating acombined protection set.

FIG. 9 illustrates an example high-level architecture 900 of an ECS™cluster, according to an aspect of the subject disclosure. ECS™ cancomprise a software-defined, cloud-scale, object storage platform thatcombines the cost advantages of commodity infrastructure with thereliability, availability and serviceability of traditional arrays. WithECS™, an organization can deliver scalable and simple public cloudservices with the reliability and control of a private-cloudinfrastructure. ECS™ provides comprehensive protocol support forunstructured (object and/or file) workloads on a single, cloud-scalestorage platform. In an aspect, the ECS™ cluster 902 can comprisemultiple nodes 904 ₁-904 _(M), wherein M is most any integer. It isnoted that the zones 102, and/or zone(s) 118, can comprise at least aportion of ECS™ cluster 902. The nodes 904 ₁-904 _(M) can comprisestorage devices (e.g. hard drives) 906 ₁-906 _(M) and can run a set ofservices 908 ₁-908 _(M). For example, single node that runs ECS™ version3.0 can manage 20 independent services. Further, ECS™ data/managementclients 910 can be coupled to the nodes 904 ₁-904 _(M).

The ECS™ cluster 902 does not protect user data with traditional schemeslike mirroring or parity protection. Instead, the ECS™ cluster 902utilizes a k+m erasure coding protection scheme, wherein a data block(e.g., data chunk) is divided into k data fragments and m codingfragments are created (e.g., by encoding the k data fragments). Encodingis performed in a manner such that the ECS™ cluster 902 can tolerate theloss of any m fragments. As an example, the default scheme for ECS™ is12+4, i.e. k equals to 12 and m equals to 4; however, the subjectdisclosure is not limited to this erasure coding protection scheme. Whensome fragments are lost, the missing fragments are restored via adecoding operation.

In one aspect, the storage services 908 ₁-908 _(M) can handle dataavailability and protection against data corruption, hardware failures,and/or data center disasters. As an example, the storage services 908₁-908 _(M) can comprise an unstructured storage engine (USE) (notshown), which is a distributed shared service that runs on each node 904₁-904 _(M) and manages transactions and persists data to nodes. The USEenables global namespace management across geographically dispersed datacenters through geo-replication. In an aspect, the USE can write allobject-related data (such as, user data, metadata, object location data)to logical containers of contiguous disk space known as chunks. Chunksare open and accepting writes or closed and not accepting writes. Afterchunks are closed, the USE can erasure-code them. The USE can write tochunks in an append-only pattern so that existing data is neveroverwritten or modified. This strategy improves performance becauselocking and cache validation is not required for I/O operations. Allnodes 904 ₁-904 _(M) can process write requests for the same objectsimultaneously while writing to different chunks.

ECS™ continuously monitors the health of the nodes 904 ₁-904 _(M), theirdisks, and objects stored in the cluster. ECS™ disperses data protectionresponsibilities across the cluster, it can automatically re-protectat-risk objects when nodes or disks fail. When there is a failure of anode or drive in the site, the USE can identify the chunks and/orerasure coded fragments affected by the failure and can write copies ofthe affected chunks and/or erasure coded fragments to good nodes anddisks that do not currently have copies.

Private and hybrid clouds greatly interest customers, who are facingever-increasing amounts of data and storage costs, particularly in thepublic cloud space. ECS™ provides a scale-out and geo-distributedarchitecture that delivers an on-premise cloud platform that scales toexabytes of data with a TCO (Total Cost of Ownership) that'ssignificantly less than public cloud storage. Further, ECS™ providesversatility, hyper-scalability, powerful features, and use of low-costindustry standard hardware.

Referring now to FIG. 10, there is illustrated a block diagram of anexample computer operable to execute data deletion with distributederasure coding. In order to provide additional context for variousaspects of the disclosed subject matter, FIG. 10 and the followingdiscussion are intended to provide a brief, general description of asuitable computing environment 1000 in which the various aspects of thespecification can be implemented. While the specification has beendescribed above in the general context of computer-executableinstructions that can run on one or more computers, those skilled in theart will recognize that the specification also can be implemented incombination with other program modules and/or as a combination ofhardware and software.

Generally, program modules include routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will appreciatethat the inventive methods can be practiced with other computer systemconfigurations, including single-processor or multiprocessor computersystems, minicomputers, mainframe computers, as well as personalcomputers, hand-held computing devices, microprocessor-based orprogrammable consumer electronics, and the like, each of which can beoperatively coupled to one or more associated devices. The illustratedaspects of the specification can also be practiced in distributedcomputing environments where certain tasks are performed by remoteprocessing devices that are linked through a communications network. Ina distributed computing environment, program modules can be located inboth local and remote memory storage devices.

Computing devices typically include a variety of media, which caninclude computer-readable storage media and/or communications media,which two terms are used herein differently from one another as follows.Computer-readable storage media can be any available storage media thatcan be accessed by the computer and includes both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media can be implementedin connection with any method or technology for storage of informationsuch as computer-readable instructions, program modules, structureddata, or unstructured data. Computer-readable storage media can include,but are not limited to, RAM, ROM, EEPROM, flash memory or other memorytechnology, CD-ROM, digital versatile disk (DVD) or other optical diskstorage, magnetic cassettes, magnetic tape, magnetic disk storage orother magnetic storage devices, or other tangible and/or non-transitorymedia which can be used to store desired information. Computer-readablestorage media can be accessed by one or more local or remote computingdevices, e.g., via access requests, queries or other data retrievalprotocols, for a variety of operations with respect to the informationstored by the medium.

Communications media typically embody computer-readable instructions,data structures, program modules or other structured or unstructureddata in a data signal such as a modulated data signal, (e.g., a carrierwave or other transport mechanism), and includes any informationdelivery or transport media. The term “modulated data signal” or signalsrefers to a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in one or moresignals. By way of example, and not limitation, communication mediainclude wired media, such as a wired network or direct-wired connection,and wireless media such as acoustic, radio frequency (RF), infrared andother wireless media.

With reference to FIG. 10, a block diagram of a computing system 1000operable to execute the disclosed systems and methods is illustrated, inaccordance with an embodiment. Computer 1012 comprises a processing unit1014, a system memory 1016, and a system bus 1018. As an example, thecomponent(s), server(s), client(s), node(s), cluster(s), system(s),zone(s), module(s), agent(s), engine(s), manager(s), and/or device(s)disclosed herein with respect to systems 100-600 and 900 can eachinclude at least a portion of the computing system 1000. System bus 1018couples system components comprising, but not limited to, system memory1016 to processing unit 1014. Processing unit 1014 can be any of variousavailable processors. Dual microprocessors and other multiprocessorarchitectures also can be employed as processing unit 1014.

System bus 1018 can be any of several types of bus structure(s)comprising a memory bus or a memory controller, a peripheral bus or anexternal bus, and/or a local bus using any variety of available busarchitectures comprising, but not limited to, industrial standardarchitecture (ISA), micro-channel architecture (MSA), extended ISA(EISA), intelligent drive electronics (IDE), VESA local bus (VLB),peripheral component interconnect (PCI), card bus, universal serial bus(USB), advanced graphics port (AGP), personal computer memory cardinternational association bus (PCMCIA), Firewire (IEEE 1394), smallcomputer systems interface (SCSI), and/or controller area network (CAN)bus used in vehicles.

System memory 1016 comprises volatile memory 1020 and nonvolatile memory1022. A basic input/output system (BIOS), comprising routines totransfer information between elements within computer 1012, such asduring start-up, can be stored in nonvolatile memory 1022. By way ofillustration, and not limitation, nonvolatile memory 1022 can compriseROM, PROM, EPROM, EEPROM, or flash memory. Volatile memory 1020comprises RAM, which acts as external cache memory. By way ofillustration and not limitation, RAM is available in many forms such asSRAM, dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rateSDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM),Rambus direct RAM (RDRAM), direct Rambus dynamic RAM (DRDRAM), andRambus dynamic RAM (RDRAM).

Computer 1012 also comprises removable/non-removable,volatile/non-volatile computer storage media. FIG. 10 illustrates, forexample, disk storage 1024. Disk storage 1024 comprises, but is notlimited to, devices like a magnetic disk drive, floppy disk drive, tapedrive, Jaz drive, Zip drive, LS-100 drive, flash memory card, or memorystick. In addition, disk storage 1024 can comprise storage mediaseparately or in combination with other storage media comprising, butnot limited to, an optical disk drive such as a compact disk ROM device(CD-ROM), CD recordable drive (CD-R Drive), CD rewritable drive (CD-RWDrive) or a digital versatile disk ROM drive (DVD-ROM). To facilitateconnection of the disk storage devices 1024 to system bus 1018, aremovable or non-removable interface is typically used, such asinterface 1026.

It is to be appreciated that FIG. 10 describes software that acts as anintermediary between users and computer resources described in suitableoperating environment 1000. Such software comprises an operating system1028. Operating system 1028, which can be stored on disk storage 1024,acts to control and allocate resources of computer system 1012. Systemapplications 1030 take advantage of the management of resources byoperating system 1028 through program modules 1032 and program data 1034stored either in system memory 1016 or on disk storage 1024. It is to beappreciated that the disclosed subject matter can be implemented withvarious operating systems or combinations of operating systems.

A user can enter commands or information into computer 1012 throughinput device(s) 1036. Input devices 1036 comprise, but are not limitedto, a pointing device such as a mouse, trackball, stylus, touch pad,keyboard, microphone, joystick, game pad, satellite dish, scanner, TVtuner card, digital camera, digital video camera, web camera, cellularphone, user equipment, smartphone, and the like. These and other inputdevices connect to processing unit 1014 through system bus 1018 viainterface port(s) 1038. Interface port(s) 1038 comprise, for example, aserial port, a parallel port, a game port, a universal serial bus (USB),a wireless based port, e.g., Wi-Fi, Bluetooth®, etc. Output device(s)1040 use some of the same type of ports as input device(s) 1036.

Thus, for example, a USB port can be used to provide input to computer1012 and to output information from computer 1012 to an output device1040. Output adapter 1042 is provided to illustrate that there are someoutput devices 1040, like display devices, light projection devices,monitors, speakers, and printers, among other output devices 1040, whichuse special adapters. Output adapters 1042 comprise, by way ofillustration and not limitation, video and sound devices, cards, etc.that provide means of connection between output device 1040 and systembus 1018. It should be noted that other devices and/or systems ofdevices provide both input and output capabilities such as remotecomputer(s) 1044.

Computer 1012 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer(s)1044. Remote computer(s) 1044 can be a personal computer, a server, arouter, a network PC, a workstation, a microprocessor based appliance, apeer device, or other common network node and the like, and typicallycomprises many or all of the elements described relative to computer1012.

For purposes of brevity, only a memory storage device 1046 isillustrated with remote computer(s) 1044. Remote computer(s) 1044 islogically connected to computer 1012 through a network interface 1048and then physically and/or wirelessly connected via communicationconnection 1050. Network interface 1048 encompasses wire and/or wirelesscommunication networks such as local-area networks (LAN) and wide-areanetworks (WAN). LAN technologies comprise fiber distributed datainterface (FDDI), copper distributed data interface (CDDI), Ethernet,token ring and the like. WAN technologies comprise, but are not limitedto, point-to-point links, circuit switching networks like integratedservices digital networks (ISDN) and variations thereon, packetswitching networks, and digital subscriber lines (DSL).

Communication connection(s) 1050 refer(s) to hardware/software employedto connect network interface 1048 to bus 1018. While communicationconnection 1050 is shown for illustrative clarity inside computer 1012,it can also be external to computer 1012. The hardware/software forconnection to network interface 1048 can comprise, for example, internaland external technologies such as modems, comprising regular telephonegrade modems, cable modems and DSL modems, wireless modems, ISDNadapters, and Ethernet cards.

The computer 1012 can operate in a networked environment using logicalconnections via wired and/or wireless communications to one or moreremote computers, cellular based devices, user equipment, smartphones,or other computing devices, such as workstations, server computers,routers, personal computers, portable computers, microprocessor-basedentertainment appliances, peer devices or other common network nodes,etc. The computer 1012 can connect to other devices/networks by way ofantenna, port, network interface adaptor, wireless access point, modem,and/or the like.

The computer 1012 is operable to communicate with any wireless devicesor entities operatively disposed in wireless communication, e.g., aprinter, scanner, desktop and/or portable computer, portable dataassistant, communications satellite, user equipment, cellular basedevice, smartphone, any piece of equipment or location associated with awireles sly detectable tag (e.g., scanner, a kiosk, news stand,restroom), and telephone. This comprises at least Wi-Fi and Bluetooth®wireless technologies. Thus, the communication can be a predefinedstructure as with a conventional network or simply an ad hoccommunication between at least two devices.

The computing system 1000 is operable to communicate with any wirelessdevices or entities operatively disposed in wireless communication,e.g., desktop and/or portable computer, server, communicationssatellite, etc. This includes at least Wi-Fi and Bluetooth® wirelesstechnologies. Thus, the communication can be a predefined structure aswith a conventional network or simply an ad hoc communication between atleast two devices.

Wi-Fi, or Wireless Fidelity, allows connection to the Internet from acouch at home, a bed in a hotel room, or a conference room at work,without wires. Wi-Fi is a wireless technology similar to that used in acell phone that enables such devices, e.g., computers, to send andreceive data indoors and out; anywhere within the range of a basestation. Wi-Fi networks use radio technologies called IEEE 802.11 (a, b,g, n, etc.) to provide secure, reliable, fast wireless connectivity. AWi-Fi network can be used to connect computers to each other, to theInternet, and to wired networks (which use IEEE 802.3 or Ethernet).Wi-Fi networks operate in the unlicensed 5 GHz radio band at a 54 Mbps(802.11a) data rate, and/or a 2.4 GHz radio band at an 11 Mbps(802.11b), a 54 Mbps (802.11g) data rate, or up to a 600 Mbps (802.11n)data rate for example, or with products that contain both bands (dualband), so the networks can provide real-world performance similar to thebasic 10 BaseT wired Ethernet networks used in many offices.

As it employed in the subject specification, the term “processor” canrefer to substantially any computing processing unit or devicecomprising, but not limited to comprising, single-core processors;single-processors with software multithread execution capability;multi-core processors; multi-core processors with software multithreadexecution capability; multi-core processors with hardware multithreadtechnology; parallel platforms; and parallel platforms with distributedshared memory in a single machine or multiple machines. Additionally, aprocessor can refer to an integrated circuit, a state machine, anapplication specific integrated circuit (ASIC), a digital signalprocessor (DSP), a programmable gate array (PGA) including a fieldprogrammable gate array (FPGA), a programmable logic controller (PLC), acomplex programmable logic device (CPLD), a discrete gate or transistorlogic, discrete hardware components, or any combination thereof designedto perform the functions described herein. Processors can exploitnano-scale architectures such as, but not limited to, molecular andquantum-dot based transistors, switches and gates, in order to optimizespace usage or enhance performance of user equipment. A processor mayalso be implemented as a combination of computing processing units. Oneor more processors can be utilized in supporting a virtualized computingenvironment. The virtualized computing environment may support one ormore virtual machines representing computers, servers, or othercomputing devices. In such virtualized virtual machines, components suchas processors and storage devices may be virtualized or logicallyrepresented. In an aspect, when a processor executes instructions toperform “operations”, this could include the processor performing theoperations directly and/or facilitating, directing, or cooperating withanother device or component to perform the operations

In the subject specification, terms such as “data store,” data storage,”“database,” “cache,” and substantially any other information storagecomponent relevant to operation and functionality of a component, referto “memory components,” or entities embodied in a “memory” or componentscomprising the memory. It is noted that the memory components, orcomputer-readable storage media, described herein can be either volatilememory or nonvolatile memory, or can include both volatile andnonvolatile memory. By way of illustration, and not limitation,nonvolatile memory can include read only memory (ROM), programmable ROM(PROM), electrically programmable ROM (EPROM), electrically erasable ROM(EEPROM), or flash memory. Volatile memory can include random accessmemory (RAM), which acts as external cache memory. By way ofillustration and not limitation, RAM is available in many forms such assynchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM),double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), SynchlinkDRAM (SLDRAM), and direct Rambus RAM (DRRAM). Additionally, thedisclosed memory components of systems or methods herein are intended tocomprise, without being limited to comprising, these and any othersuitable types of memory.

The illustrated aspects of the disclosure can be practiced indistributed computing environments where certain tasks are performed byremote processing devices that are linked through a communicationsnetwork. In a distributed computing environment, program modules can belocated in both local and remote memory storage devices.

The systems and processes described above can be embodied withinhardware, such as a single integrated circuit (IC) chip, multiple ICs,an application specific integrated circuit (ASIC), or the like. Further,the order in which some or all of the process blocks appear in eachprocess should not be deemed limiting. Rather, it should be understoodthat some of the process blocks can be executed in a variety of ordersthat are not all of which may be explicitly illustrated herein.

As used in this application, the terms “component,” “module,” “system,”“interface,” “cluster,” “server,” “node,” or the like are generallyintended to refer to a computer-related entity, either hardware, acombination of hardware and software, software, or software in executionor an entity related to an operational machine with one or more specificfunctionalities. For example, a component can be, but is not limited tobeing, a process running on a processor, a processor, an object, anexecutable, a thread of execution, computer-executable instruction(s), aprogram, and/or a computer. By way of illustration, both an applicationrunning on a controller and the controller can be a component. One ormore components may reside within a process and/or thread of executionand a component may be localized on one computer and/or distributedbetween two or more computers. As another example, an interface caninclude input/output (I/O) components as well as associated processor,application, and/or API components.

Furthermore, the terms “user,” “consumer,” “client,” and the like areemployed interchangeably throughout the subject specification, unlesscontext warrants particular distinction(s) among the terms. It is notedthat such terms can refer to human entities or automatedcomponents/devices supported through artificial intelligence (e.g., acapacity to make inference based on complex mathematical formalisms),which can provide simulated vision, sound recognition and so forth.

Further, the various embodiments can be implemented as a method,apparatus, or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware, or anycombination thereof to control a computer to implement one or moreaspects of the disclosed subject matter. An article of manufacture canencompass a computer program accessible from any computer-readabledevice or computer-readable storage/communications media. For example,computer readable storage media can include but are not limited tomagnetic storage devices (e.g., hard disk, floppy disk, magnetic strips. . . ), optical disks (e.g., compact disk (CD), digital versatile disk(DVD) . . . ), smart cards, and flash memory devices (e.g., card, stick,key drive...). Of course, those skilled in the art will recognize manymodifications can be made to this configuration without departing fromthe scope or spirit of the various embodiments.

Artificial intelligence based systems, e.g., utilizing explicitly and/orimplicitly trained classifiers, can be employed in connection withperforming inference and/or probabilistic determinations and/orstatistical-based determinations as in accordance with one or moreaspects of the disclosed subject matter as described herein. Forexample, an artificial intelligence system can be used to dynamicallyperform operations as described herein.

A classifier can be a function that maps an input attribute vector,x=(x1, x2, x3, x4, xn), to a confidence that the input belongs to aclass, that is, f(x)=confidence (class). Such classification can employa probabilistic and/or statistical-based analysis (e.g., factoring intothe analysis utilities and costs) to infer an action that a user desiresto be automatically performed. In the case of communication systems, forexample, attributes can be information received from access points,servers, components of a wireless communication network, etc., and theclasses can be categories or areas of interest (e.g., levels ofpriorities). A support vector machine is an example of a classifier thatcan be employed. The support vector machine operates by finding ahypersurface in the space of possible inputs, which the hypersurfaceattempts to split the triggering criteria from the non-triggeringevents. Intuitively, this makes the classification correct for testingdata that is near, but not identical to training data. Other directedand undirected model classification approaches include, e.g., naïveBayes, Bayesian networks, decision trees, neural networks, fuzzy logicmodels, and probabilistic classification models providing differentpatterns of independence can be employed. Classification as used hereincan also be inclusive of statistical regression that is utilized todevelop models of priority.

In accordance with various aspects of the subject specification,artificial intelligence based systems, components, etc. can employclassifiers that are explicitly trained, e.g., via a generic trainingdata, etc. as well as implicitly trained, e.g., via observingcharacteristics of communication equipment, e.g., a server, etc.,receiving reports from such communication equipment, receiving operatorpreferences, receiving historical information, receiving extrinsicinformation, etc. For example, support vector machines can be configuredvia a learning or training phase within a classifier constructor andfeature selection module. Thus, the classifier(s) can be used by anartificial intelligence system to automatically learn and perform anumber of functions.

In addition, the word “example” or “exemplary” is used herein to meanserving as an example, instance, or illustration. Any aspect or designdescribed herein as “exemplary” is not necessarily to be construed aspreferred or advantageous over other aspects or designs. Rather, use ofthe word exemplary is intended to present concepts in a concretefashion. As used in this application, the term “or” is intended to meanan inclusive “or” rather than an exclusive “or.” That is, unlessspecified otherwise, or clear from context, “X employs A or B” isintended to mean any of the natural inclusive permutations. That is, ifX employs A; X employs B; or X employs both A and B, then “X employs Aor B” is satisfied under any of the foregoing instances. In addition,the articles “a” and “an” as used in this application and the appendedclaims should generally be construed to mean “one or more” unlessspecified otherwise or clear from context to be directed to a singularform.

What has been described above includes examples of the presentspecification. It is, of course, not possible to describe everyconceivable combination of components or methods for purposes ofdescribing the present specification, but one of ordinary skill in theart may recognize that many further combinations and permutations of thepresent specification are possible. Accordingly, the presentspecification is intended to embrace all such alterations, modificationsand variations that fall within the spirit and scope of the appendedclaims. Furthermore, to the extent that the term “includes” is used ineither the detailed description or the claims, such term is intended tobe inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim.

What is claimed is:
 1. A system, comprising: a processor; and a memorythat stores executable instructions that, when executed by theprocessor, facilitate performance of operations, comprising: selectingsource chunks of data stored within a storage system that are determinedto have fewer than a defined number of data fragments, wherein a firstsource chunk, of the source chunks, is divided into first indexed datafragments, and wherein the first indexed data fragments areerasure-coded to generate first coding fragments associated with thefirst source chunk; modifying an index of a data fragment of the firstindexed data fragments and modifying the first coding fragments of thefirst source chunk to convert the source chunks into complementarysource chunks, wherein the complementary source chunks do not have datafragments with a common index, and wherein the modifying the firstcoding fragments generates modified coding fragments independent ofperforming an erasure-coding operation; and subsequent to the modifying,generating a meta chunk based on combining the complementary sourcechunks, wherein combined coding fragments, associated with the metachunk, are determined based on adding the modified coding fragments withsecond coding fragments generated based on erasure coding second indexeddata fragments of a second source chunk of the source chunks.
 2. Thesystem of claim 1, wherein the operations further comprise: linking thesource chunks to the meta chunk.
 3. The system of claim 2, wherein thelinking comprises updating metadata associated with one of the sourcechunks to comprise reference data indicative of the meta chunk.
 4. Thesystem of claim 1, wherein the operations further comprise: storing thecombined coding fragments; and subsequent to the storing, deleting thefirst coding fragments, the second coding fragments, and the modifiedcoding fragments.
 5. The system of claim 1, wherein the meta chunk is afirst meta chunk and the source chunks are first source chunks, whereinthe first source chunks comprise a second meta chunk that has beengenerated based on a combination of second source chunks, and whereinthe second source chunks are determined to have fewer than the definednumber of data fragments.
 6. The system of claim 1, wherein themodifying the first coding fragments comprises modifying the firstcoding fragments based on subtracting first content and adding secondcontent to a first coding fragment of the first coding fragments,wherein the first content is determined by multiplying the data fragmentby a first coefficient from a coding matrix, and wherein the secondcontent determined by multiplying the data fragment by a secondcoefficient from the coding matrix.
 7. The system of claim 1, whereinthe combined coding fragments are employable to recover at least aportion of the source chunks.
 8. The system of claim 1, wherein thefirst indexed data fragments comprise first indices that are assigned toincrease a probability that the two or more of the source chunks do nothave the data fragments with the common index.
 9. The system of claim 1,wherein physical capacity is not allocated for the meta chunk.
 10. Thesystem of claim 1, wherein the storage system comprises ageographically-distributed object storage system.
 11. A method,comprising: determining, by a system comprising a processor, sourcechunks of data stored within a storage system that have fewer than adefined number of data fragments, wherein a first source chunk, of thesource chunks, is divided into first indexed data fragments, and whereinthe first indexed data fragments are erasure-coded to generate firstcoding fragments associated with the first source chunk; adjusting anindex of a data fragment of the first indexed data fragments andupdating the first coding fragments of the first source chunk to convertthe source chunks into complementary source chunks, wherein thecomplementary source chunks do not have data fragments with a commonindex, and wherein the modifying the first coding fragments generatesupdated coding fragments independent of performing an erasure-codingoperation; and subsequent to the updating, combining the complementarysource chunks to generate a meta chunk, wherein combined codingfragments, associated with the meta chunk, are determined based onadding the updated coding fragments with second coding fragmentsgenerated based on erasure coding second indexed data fragments of asecond source chunk of the source chunks.
 12. The method of claim 11,wherein the updating the first coding fragments comprises updating thefirst coding fragments based on subtracting first content and addingsecond content to a first coding fragment of the first coding fragments,wherein the first content is determined by multiplying the data fragmentwith a first coefficient from a coding matrix, and wherein the secondcontent determined by multiplying the data fragment with a secondcoefficient from the coding matrix.
 13. The method of claim 11, furthercomprising: storing reference data that links the source chunks to themeta chunk.
 14. The method of claim 13, further comprising: in responseto determining that a failure condition has been satisfied, determining,based on the reference data, the meta chunk linked to at least a portionof the source chunks.
 15. The method of claim 14, further comprising:recovering at least the portion of the source chunks based on performinga decoding operation at the meta chunk level.
 16. The method of claim11, further comprising: subsequent to the determining the combinedcoding fragments, deleting the first coding fragments, the second codingfragments, and the updated coding fragments.
 17. A computer-readablestorage medium comprising instructions that, in response to execution,cause a server device comprising a processor to perform operations,comprising: encoding chunks of data stored in an object storage system,wherein the chunks comprise data fragments that have been assignedrespective indices, and wherein the encoding comprises combining, basedon the respective indices, the data fragments with correspondingencoding coefficients to generate respective; modifying at least onechunk of a group of the chunks to ensure that the group of the chunksdoes not comprise a corresponding group of the data fragments that havea common index, wherein the group of the chunks are determined not tohave more than a defined number of data fragments, and wherein themodifying results in a generation of updated coding fragments, thatcorrespond to the group of the chunks, independent of performing anerasure coding operation to re-encode the at least one chunk; subsequentto the modifying, combining the group of the chunks to generate a metachunk; and based on a summation of the updated coding fragments,determining meta chunk coding fragments that are to be employed torecover at least a portion of the group of the chunks during a failurecondition.
 18. The computer-readable storage medium of claim 17, whereinthe modifying the at least one chunk comprises altering an indexassigned to a data fragment of the at least one chunk.
 19. Thecomputer-readable storage medium of claim 17, wherein the operationsfurther comprise: determining reference data that links the group of thechunks to the meta chunk.
 20. The computer-readable storage medium ofclaim 19, wherein the operations further comprise: updating metadataassociated with the group of the chunks based on the reference data.